Introduction: Many studies have shown how feedback can improve perceptual learning (e.g. Herzog and Fahle, 1997). Less is known about how well humans use feedback. To investigate this question, we use an experimental paradigm (rapid perceptual learning, RPL; Abbey et al., 2001; Eckstein et al., 2002) in which an ideal observer learns from trial to trial. The framework allows us to compare the amount of learning with and without feedback in the human observer to the maximal possible learning assessed by the ideal observer. Methods: In the present RPL paradigm a learning set consisted of 8 trials. One out of the 26 letters from the English alphabet was randomly chosen and remained as a target throughout a learning set. On each trial, the target letter appeared randomly in 1 out of 8 locations embedded in image noise. The observers had to localize the target on each trial and identify it on the last trial of the learning set. There were two blocked conditions: 1) no feedback, 2) feedback about the target location provided with a post-cue following the observers' localization response (post-cue feedback). Observers participated in 1200 learning sets. Results: Human localization performance across the 8 learning trials increased significantly for both conditions (averaged across conditions and observers = 8 %). Learning was greater for the feedback condition than the no feedback condition consistent with previous studies. Overall human efficiency (i.e. the squared ratio of the ideal observer contrast threshold and the human contrast threshold) decreased (>>7 %) from the first to the last learning trial for both conditions suggesting that humans learn less than the ideal observer. Efficiency for the feedback condition reached its lowest point in the 2nd learning trial suggesting that the ideal observer learns faster with feedback than humans. Conclusion: Humans use of feedback in a perceptual learning task is imperfect and slow compared to the ideal observer.